Please use this identifier to cite or link to this item:
http://dspace.azjhpc.org/xmlui/handle/123456789/268
Title: | Predictive Modeling of Click-Through Rates: A Regression Analysis Approach |
Authors: | Suleymanzade, Suleyman |
Keywords: | Data Splitting;CTR-related;XGBoost;CTR Prediction |
Issue Date: | 1-Dec-2023 |
Publisher: | Azerbaijan Journal of High Performance Computing |
Abstract: | This research uses advanced regression techniques to develop a robust predictive model for Click-Through Rates (CTR) in online advertising. The study leverages a diverse dataset encompassing various advertising campaigns and user interactions to uncover patterns and relationships influencing click-through behavior. The goal is to provide advertisers with a tool for accurate CTR prediction, enabling them to optimize campaigns and allocate resources effectively. |
URI: | http://dspace.azjhpc.org/xmlui/handle/123456789/268 |
ISSN: | 2616-6127 2617-4383 |
Journal Title: | Azerbaijan Journal of High Performance Computing |
Volume: | 6 |
Issue: | 2 |
First page number: | 199 |
Last page number: | 202 |
Number of pages: | 4 |
Appears in Collections: | Azerbaijan Journal of High Performance Computing |
Files in This Item:
File | Description | Size | Format | |
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doi.org.10.32010.26166127.2023.6.2.199.202.pdf | 567.66 kB | Adobe PDF | View/Open |
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